Variants detected by DeepVariant in cNF cell lines
Robert Allaway
05 October, 2022
1 Setup
First, we set up our workspace. Load maftools to analyze the maf files.
Get all the mafs and merge them into one big maf.
library(maftools)
library(tidyverse)
library(synapser)
synLogin()## Welcome, Robert Allaway!## NULLchild <- synGetChildren('syn35817148')$asList()
ids <- map(child, "id") %>% unlist()
maf_paths <- lapply(ids, synGet)##
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Downloading [####################]100.00% 38.4MB/38.4MB (2.8MB/s) DeepVariant_icNF98.4d.vep.maf.synapse_download_102759046 Done...mafs <- lapply(maf_paths, function(x){
maftools::read.maf(x$path, rmFlags=100)
})## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 37421
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 2.006s elapsed (1.741s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 38005
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.735s elapsed (1.466s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 39115
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.795s elapsed (1.522s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 35826
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.079s elapsed (0.850s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 37896
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.649s elapsed (1.301s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 38870
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.412s elapsed (1.085s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 35027
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.000s elapsed (0.737s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 38500
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.492s elapsed (1.237s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 39704
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.536s elapsed (1.177s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 35993
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.050s elapsed (0.759s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 44103
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.265s elapsed (0.984s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 37787
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.214s elapsed (0.960s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 38156
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.174s elapsed (0.927s cpu)
## -Reading
## -Validating
## -Removing 100 FLAG genes
## -Silent variants: 38833
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 1.399s elapsed (1.154s cpu)merged_maf <- merge_mafs(mafs)## Merging 14 MAF objects
## -Validating
## -Silent variants: 535236
## -Summarizing
## -Processing clinical data
## -Finished in 5.778s elapsed (5.094s cpu)2 Compare mafs
First, let’s compare the immortalized vs primary cells to see if there are any significant differentially mutated genes. There do not appear to be (lowest p-value ~0.1 and all p-values = 1 after FDR correction). With more cell lines, we might be able to detect a difference here, but with the current experiment we don’t detect any significantly differentially mutated genes.
primary_mafs <- subsetMaf(merged_maf, tsb = c("28cNF","cNF00.10a","cNF04.9a","cNF97.2a","cNF97.2b","cNF98.4c","cNF98.4d"))## -Processing clinical dataimmortalized_mafs <- subsetMaf(merged_maf, tsb = c("i28cNF","icNF00.10a","icNF04.9a","icNF97.2a","icNF97.2b","icNF98.4c","icNF98.4d"))## -Processing clinical datacompare_mafs <- mafCompare(primary_mafs,immortalized_mafs, m1Name = "Primary", m2Name = "Immortalized", minMut = 0)
compare_mafs[1]## $results
## Hugo_Symbol Primary Immortalized pval or ci.up
## 1: PTPRZ1 0 5 0.02097902 0.00000000 0.6899019
## 2: ALPG 1 5 0.10256410 0.08534283 1.3882351
## 3: ANXA8L1 0 3 0.19230769 0.00000000 2.1669275
## 4: FAM90A11P 0 3 0.19230769 0.00000000 2.1669275
## 5: OR4N4 0 3 0.19230769 0.00000000 2.1669275
## ---
## 4233: ZSCAN5A 1 1 1.00000000 1.00000000 91.3510967
## 4234: ZSWIM6 4 4 1.00000000 1.00000000 13.0293597
## 4235: ZWINT 6 6 1.00000000 1.00000000 91.3510967
## 4236: ZYX 3 3 1.00000000 1.00000000 13.0293597
## 4237: ZZEF1 1 1 1.00000000 1.00000000 91.3510967
## ci.low adjPval
## 1: 0.000000000 1
## 2: 0.001222521 1
## 3: 0.000000000 1
## 4: 0.000000000 1
## 5: 0.000000000 1
## ---
## 4233: 0.010946776 1
## 4234: 0.076749743 1
## 4235: 0.010946776 1
## 4236: 0.076749743 1
## 4237: 0.010946776 13 Plot maf summaries
Let’s plot a summary of each maf.
lapply(mafs, function(x){
plotmafSummary(x)
})
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## NULL4 Plot maf summaries
Let’s plot a summary of each cohort.
p <- plotmafSummary(primary_mafs, showBarcodes = T, textSize = 0.5)
p## NULLpdf('../figures/DeepVariant_primaryMafSummary.pdf')
p## NULLdev.off()## quartz_off_screen
## 2p <- plotmafSummary(immortalized_mafs, showBarcodes = T, textSize = 0.5)
p## NULLpdf('../figures/DeepVariant_immortalizedMafSummary.pdf')
p## NULLdev.off()## quartz_off_screen
## 24.1 Look at NF1 variants
lollipopPlot2(primary_mafs, immortalized_mafs, gene = 'NF1',m1_label = 'all', m2_label = 'all',labPosAngle = 45, labPosSize = 0.75, domainLabelSize = 0.5, m1_name = "Primary", m2_name = "Immortalized")## HGNC refseq.ID protein.ID aa.length
## 1: NF1 NM_000267 NP_000258 2818
## 2: NF1 NM_001042492 NP_001035957 2839
## HGNC refseq.ID protein.ID aa.length
## 1: NF1 NM_000267 NP_000258 2818
## 2: NF1 NM_001042492 NP_001035957 2839
pdf("../figures/DeepVariant_NF1_variants.pdf")
lollipopPlot2(primary_mafs, immortalized_mafs, gene = 'NF1',m1_label = 'all', m2_label = 'all',labPosAngle = 45, labPosSize = 0.75, domainLabelSize = 0.5, m1_name = "Primary", m2_name = "Immortalized")## HGNC refseq.ID protein.ID aa.length
## 1: NF1 NM_000267 NP_000258 2818
## 2: NF1 NM_001042492 NP_001035957 2839
## HGNC refseq.ID protein.ID aa.length
## 1: NF1 NM_000267 NP_000258 2818
## 2: NF1 NM_001042492 NP_001035957 2839dev.off()## quartz_off_screen
## 24.2 Table of NF1 variants
merged_maf@data %>%
filter(Hugo_Symbol == "NF1") %>%
select(Tumor_Sample_Barcode, Variant_Classification, Variant_Type,
HGVSc, HGVSp_Short,
dbSNP_RS, SIFT, PolyPhen, IMPACT) %>%
set_names(c("Cell Line", "Variant Classification", "Variant Type",
"Genetic Change", "Protein Change", "dbSNP ID", "SIFT", "PolyPhen", "IMPACT")) %>%
write_csv("../figures/DeepVariant_NF1_variants.csv")
merged_maf@data %>%
filter(Hugo_Symbol == "NF1") %>%
select(Tumor_Sample_Barcode, Variant_Classification, Variant_Type,
HGVSc, HGVSp_Short,
dbSNP_RS, SIFT, PolyPhen, IMPACT) %>%
set_names(c("Cell Line", "Variant Classification", "Variant Type",
"Genetic Change", "Protein Change", "dbSNP ID", "SIFT", "PolyPhen", "IMPACT"))sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] synapser_0.15.35 forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
## [5] purrr_0.3.4 readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
## [9] ggplot2_3.3.6 tidyverse_1.3.2 maftools_2.12.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.4 sass_0.4.2 bit64_4.0.5
## [4] vroom_1.6.0 jsonlite_1.8.2 splines_4.2.1
## [7] pack_0.1-1 modelr_0.1.9 bslib_0.4.0
## [10] assertthat_0.2.1 highr_0.9 googlesheets4_1.0.1
## [13] cellranger_1.1.0 yaml_2.3.5 pillar_1.8.1
## [16] backports_1.4.1 lattice_0.20-45 glue_1.6.2
## [19] digest_0.6.29 RColorBrewer_1.1-3 rvest_1.0.3
## [22] colorspace_2.0-3 htmltools_0.5.3 Matrix_1.5-1
## [25] pkgconfig_2.0.3 PythonEmbedInR_0.12.79 broom_1.0.1
## [28] haven_2.5.1 scales_1.2.1 tzdb_0.3.0
## [31] googledrive_2.0.0 generics_0.1.3 ellipsis_0.3.2
## [34] cachem_1.0.6 withr_2.5.0 cli_3.4.1
## [37] survival_3.4-0 magrittr_2.0.3 crayon_1.5.2
## [40] readxl_1.4.1 evaluate_0.16 fs_1.5.2
## [43] fansi_1.0.3 xml2_1.3.3 tools_4.2.1
## [46] data.table_1.14.2 hms_1.1.2 gargle_1.2.1
## [49] lifecycle_1.0.2 munsell_0.5.0 reprex_2.0.2
## [52] compiler_4.2.1 jquerylib_0.1.4 rlang_1.0.6
## [55] grid_4.2.1 rstudioapi_0.14 rmarkdown_2.16
## [58] DNAcopy_1.70.0 gtable_0.3.1 codetools_0.2-18
## [61] DBI_1.1.3 R6_2.5.1 lubridate_1.8.0
## [64] knitr_1.40 bit_4.0.4 fastmap_1.1.0
## [67] utf8_1.2.2 stringi_1.7.8 parallel_4.2.1
## [70] vctrs_0.4.2 dbplyr_2.2.1 tidyselect_1.1.2
## [73] xfun_0.33